Abstract
Business process models are widely pronounced as valuable assets for every organization. Given that manual management of these process models requires substantial human effort, process model repositories have been developed to effectively manage these models. The key features of these repositories include process model searching, clone detection, duplicate avoidance, and process harmonization. The usefulness of all these features depends upon the accuracy of the underlying process model matching technique. Process Model Matching (PMM) refers to identifying corresponding activities between a pair of process models that represent the same or similar functionality. Recognizing the importance of PMM, a plethora of matching techniques have been developed. Despite the presence of these techniques, the need for enhancing the accuracy of PMM have been widely pronounced in recent studies. To that end, this paper proposes a word embeddings based approach to enhance the accuracy of PMM. For the evaluation of the proposed approach, we have used three state-of-the-art word embeddings, Word2vec, Glove, and fastText, for experiments on three benchmark datasets. The results show that the fastText generated embeddings, that are recently released by Facebook Inc., are the most suitable embeddings for process model matching.
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References
Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4
Kuss, E., Leopold, H., Van der Aa, H., Stuckenschmidt, H., Reijers, H.A.: A probabilistic evaluation procedure for process model matching techniques. Data Knowl. Eng. 117, 393–406 (2018). In press
Yan, Z., Dijkman, R., Grefen, P.: Generating synthetic process model collections with properties of labeled real-life models. In: Ouyang, C., Jung, J.-Y. (eds.) AP-BPM 2014. LNBIP, vol. 181, pp. 74–88. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08222-6_6
Yan, Z., Dijkman, R., Grefen, P.: Business process model repositories – framework and survey. Inf. Softw. Technol. 54(4), 380–395 (2012)
Makni, L., Haddar, N.Z., Ben-Abdallah, H.: Business process model matching: an approach based on semantics and structure. In: Proceedings of the 12th International Joint Conference on e-Business and Telecommunications (ICETE), Colmar, France (2015)
Kuss, E., Leopold, H., Meilicke, C., Stuckenschmidt, H.: Ranking-based evaluation of process model matching. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 298–305. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_19
Antunes, G., et al.: The process model matching contest 2015. Enterp. Modell. Inf. Syst. Archit. (2015)
Meilicke, C., Leopold, H., Kuss, E., Stuckenschmidt, H., Reijers, H.A.: Overcoming individual process model matcher weaknesses using ensemble matching. Decis. Support Syst. 100(1), 15–26 (2017)
Jabeen, F., Leopold, H., Reijers, H.A.: How to make process model matching work better? An analysis of current similarity measures. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 181–193. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_13
Sonntag, A., Hake, P., Fettke, P., Loos, P.: An approach for semantic business process model matching using supervised machine learning. In: Proceedings of the 24th European Conference on Information Systems, Istanbul, Turkey (2016)
Seok, M., Song, H.J., Park, C.Y., Kim, J.D., Kim, Y.: Comparison of NER performance using word embedding. Adv. Sci. Technol. Lett. 120(1), 784–788 (2015)
Chamberlain, B.P., Clough, J., Deisenroth, P.: Neural embeddings of graphs in hyperbolic space. https://arxiv.org/abs/1705.10359. Accessed 31 Aug 2018
Shahzad, K., Pervaiz, I., Nawab, R.M.A.: WordNet based semantic similarity measures for process model matching. In: Proceedings of the 17th International Conference on Perspectives in Business Informatics Research, CEUR Proceedings, Stockholm, Sweden, (2018)
Ehrig, M., Koschmider, A., Oberweis, A.: Measuring similarity between semantic business process models. In: Proceedings of the Fourth Asia-Pacific Conference on Conceptual Modelling, vol. 67, pp. 71–80. Australian Computer Society, Inc. (2007)
Jin, T., Wang, J., La Rosa, M., Hofstede, A., Wen, L.: Efficient querying of large process model repositories. Comput. Ind. 64(1), 41–49 (2013)
Klinkmüller, C., Weber, I., Mendling, J., Leopold, H., Ludwig, A.: Increasing recall of process model matching by improved activity label matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 211–218. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_17
WordNet. https://wordnet.princeton.edu/. Accessed 11 June 2018
Cayoglu, U., et al.: Report: the process model matching contest 2013. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 442–463. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_35
Sebu, M.L., Ciocarlie, H.: Similarity of business process models in a modular design. In: Proceedings of the 11th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 31–36 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. https://arxiv.org/abs/1310.4546. Accessed June 2018
Goldberg, Y., Levy, O.: Word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. https://arxiv.org/abs/1402.3722. Accessed June 2018
Chen, Y., Perozzi, B., Al-Rfou, R., Skiena, S.: The expressive power of word embeddings. https://arxiv.org/abs/1301.3226. Accessed June 2018
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Shahzad, K., Kanwal, S., Malik, K. (2019). Process Model Matching with Word Embeddings. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_73
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DOI: https://doi.org/10.1007/978-981-13-6052-7_73
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